A Bayesian Modified Ising Model for Identifying Spatially Variable Genes from Spatial Transcriptomics Data

TL;DR

This paper introduces a Bayesian modified Ising model to identify spatially variable genes in spatial transcriptomics data, outperforming traditional kernel-based methods and revealing novel biological insights.

Contribution

It proposes a new Bayesian Ising model approach that captures complex spatial patterns in gene expression data, overcoming limitations of existing Gaussian process-based methods.

Findings

Higher accuracy in detecting spatially variable genes compared to kernel-based methods

Discovered novel spatial patterns in real datasets

Provided biological insights into tissue structure and function

Abstract

A recent technology breakthrough in spatial molecular profiling has enabled the comprehensive molecular characterizations of single cells while preserving spatial information. It provides new opportunities to delineate how cells from different origins form tissues with distinctive structures and functions. One immediate question in spatial molecular profiling data analysis is to identify genes whose expressions exhibit spatially correlated patterns, called spatially variable genes. Most current methods to identify spatially variable genes are built upon the geostatistical model with Gaussian process to capture the spatial patterns, which rely on ad hoc kernels that could limit the models' ability to identify complex spatial patterns. In order to overcome this challenge and capture more types of spatial patterns, we introduce a Bayesian approach to identify spatially variable genes via a modified Ising model. The key idea is to use the energy interaction parameter of the Ising model to characterize spatial expression patterns. We use auxiliary variable Markov chain Monte Carlo algorithms to sample from the posterior distribution with an intractable normalizing constant in the model. Simulation studies using both simulated and synthetic data showed that the energy-based modeling approach led to higher accuracy in detecting spatially variable genes than those kernel-based methods. When applied to two real spatial transcriptomics datasets, the proposed method discovered novel spatial patterns that shed light on the biological mechanisms. In summary, the proposed method presents a new perspective for analyzing spatial transcriptomics data.